Type 2 diabetes is a complex disease that affects 1 in 10 Americans and is influenced by many common genetic risk factors. Genetic association studies have identified over 100 loci that influence T2D risk, although how these loci mechanistically contribute to diabetes pathogenesis is largely unknown. The majority of these risk loci map to non-coding sequence, and likely alter gene regulatory processes in specific cell-types. Translating this breadth of diabetes regulatory variation into their molecular mechanisms can thus profoundly inform on diabetes pathophysiology, although remains challenging. In this study we propose a novel approach to identify T2D-relevant transcription factors and gene networks regulated by these factors by combining statistical human genetics, epigenomics, high-throughput assay and quantitative trait locus (QTL) mapping. In this approach we identify T2D risk variants that affect the cell-type expression of a transcription factor gene, characterize the genomic binding sites and target gene network regulated by these transcription factors, and broadly determine the effects of variants disrupting transcription factor-regulated networks on diabetes risk. In preliminary findings we have identified several diabetes risk variants that affect the cell-type expression level of a transcription factor gene, almost none of which have known involvement in diabetes- relevant pathways. In Aim 1 we will combine genetic fine-mapping with epigenomic annotation and eQTL data from diabetes-relevant cells to identify diabetes risk variants that affect the cell- type expression of a transcription factor. In Aim 2 we will perform ChIP-seq assays of five transcription factors in pancreatic islet samples combined with eQTL data to map the trans network of target genes affected by transcription factor regulatory variants. In Aim 3 we will combine allelic imbalance mapping and in silico motif prediction of islet ChIP-seq data to quantify the genome-wide effects of variants disrupting transcription factor-regulated networks on diabetes risk. The results of these studies will reveal specific transcription factors that are regulated by diabetes risk variants, and the gene networks regulated by these factors that in turn impact diabetes pathophysiology. Together these studies will provide insight into critical transcription factors and gene networks involved in diabetes pathogenesis.